268 research outputs found
ANALYZING PULMONARY ABNORMALITY WITH SUPERPIXEL BASED GRAPH NEURAL NETWORKS IN CHEST X-RAY
In recent years, the utilization of graph-based deep learning has gained prominence, yet its potential in the realm of medical diagnosis remains relatively unexplored. Convolutional Neural Network (CNN) has achieved state-of-the-art performance in areas such as computer vision, particularly for grid-like data such as images. However, they require a huge dataset to achieve top level of performance and challenge arises when learning from the inherent irregular/unordered nature of physiological data. In this thesis, the research primarily focuses on abnormality screening: classification of Chest X-Ray (CXR) as Tuberculosis positive or negative, using Graph Neural Networks (GNN) that uses Region Adjacency Graphs (RAGs), and each superpixel serves as a dedicated graph node. For graph classification, provided that the different classes are distinct enough GNN often classify graphs using just the graph structures. This study delves into the inquiry of whether the incorporation of node features, such as coordinate points and pixel intensity, along with structured data representing graph can enhance the learning process. By integration of residual and concatenation structures, this methodology adeptly captures essential features and relationships among superpixels, thereby contributing to advancements in tuberculosis identification. We achieved the best performance: accuracy of 0.80 and AUC of 0.79, through the union of state-of-the-art neural network architectures and innovative graph-based representations. This work introduces a new perspective to medical image analysis
Semantics-Empowered Communication: A Tutorial-cum-Survey
Along with the springing up of the semantics-empowered communication (SemCom)
research, it is now witnessing an unprecedentedly growing interest towards a
wide range of aspects (e.g., theories, applications, metrics and
implementations) in both academia and industry. In this work, we primarily aim
to provide a comprehensive survey on both the background and research taxonomy,
as well as a detailed technical tutorial. Specifically, we start by reviewing
the literature and answering the "what" and "why" questions in semantic
transmissions. Afterwards, we present the ecosystems of SemCom, including
history, theories, metrics, datasets and toolkits, on top of which the taxonomy
for research directions is presented. Furthermore, we propose to categorize the
critical enabling techniques by explicit and implicit reasoning-based methods,
and elaborate on how they evolve and contribute to modern content & channel
semantics-empowered communications. Besides reviewing and summarizing the
latest efforts in SemCom, we discuss the relations with other communication
levels (e.g., conventional communications) from a holistic and unified
viewpoint. Subsequently, in order to facilitate future developments and
industrial applications, we also highlight advanced practical techniques for
boosting semantic accuracy, robustness, and large-scale scalability, just to
mention a few. Finally, we discuss the technical challenges that shed light on
future research opportunities.Comment: Submitted to an IEEE journal. Copyright might be transferred without
further notic
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Recent years have seen rapid deployment of mobile computing and Internet of
Things (IoT) networks, which can be mostly attributed to the increasing
communication and sensing capabilities of wireless systems. Big data analysis,
pervasive computing, and eventually artificial intelligence (AI) are envisaged
to be deployed on top of the IoT and create a new world featured by data-driven
AI. In this context, a novel paradigm of merging AI and wireless
communications, called Wireless AI that pushes AI frontiers to the network
edge, is widely regarded as a key enabler for future intelligent network
evolution. To this end, we present a comprehensive survey of the latest studies
in wireless AI from the data-driven perspective. Specifically, we first propose
a novel Wireless AI architecture that covers five key data-driven AI themes in
wireless networks, including Sensing AI, Network Device AI, Access AI, User
Device AI and Data-provenance AI. Then, for each data-driven AI theme, we
present an overview on the use of AI approaches to solve the emerging
data-related problems and show how AI can empower wireless network
functionalities. Particularly, compared to the other related survey papers, we
provide an in-depth discussion on the Wireless AI applications in various
data-driven domains wherein AI proves extremely useful for wireless network
design and optimization. Finally, research challenges and future visions are
also discussed to spur further research in this promising area.Comment: Accepted at the IEEE Communications Surveys & Tutorials, 42 page
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit
sequences. Such an approach provides efficient engineering designs that are
agnostic to the meanings of the messages or to the goal that the message
exchange aims to achieve. Next generation systems, however, can be potentially
enriched by folding message semantics and goals of communication into their
design. Further, these systems can be made cognizant of the context in which
communication exchange takes place, providing avenues for novel design
insights. This tutorial summarizes the efforts to date, starting from its early
adaptations, semantic-aware and task-oriented communications, covering the
foundations, algorithms and potential implementations. The focus is on
approaches that utilize information theory to provide the foundations, as well
as the significant role of learning in semantics and task-aware communications.Comment: 28 pages, 14 figure
Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures
The advent of communication technologies marks a transformative phase in
critical infrastructure construction, where the meticulous analysis of failures
becomes paramount in achieving the fundamental objectives of continuity,
security, and availability. This survey enriches the discourse on failures,
failure analysis, and countermeasures in the context of the next-generation
critical communication infrastructures. Through an exhaustive examination of
existing literature, we discern and categorize prominent research orientations
with focuses on, namely resource depletion, security vulnerabilities, and
system availability concerns. We also analyze constructive countermeasures
tailored to address identified failure scenarios and their prevention.
Furthermore, the survey emphasizes the imperative for standardization in
addressing failures related to Artificial Intelligence (AI) within the ambit of
the sixth-generation (6G) networks, accounting for the forward-looking
perspective for the envisioned intelligence of 6G network architecture. By
identifying new challenges and delineating future research directions, this
survey can help guide stakeholders toward unexplored territories, fostering
innovation and resilience in critical communication infrastructure development
and failure prevention
Neuromorphic computing using wavelength-division multiplexing
Optical neural networks (ONNs), or optical neuromorphic hardware
accelerators, have the potential to dramatically enhance the computing power
and energy efficiency of mainstream electronic processors, due to their
ultralarge bandwidths of up to 10s of terahertz together with their analog
architecture that avoids the need for reading and writing data back and forth.
Different multiplexing techniques have been employed to demonstrate ONNs,
amongst which wavelength division multiplexing (WDM) techniques make sufficient
use of the unique advantages of optics in terms of broad bandwidths. Here, we
review recent advances in WDM based ONNs, focusing on methods that use
integrated microcombs to implement ONNs. We present results for human image
processing using an optical convolution accelerator operating at 11 Tera
operations per second. The open challenges and limitations of ONNs that need to
be addressed for future applications are also discussed.Comment: 13 pages, 8 figures, 160 reference
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental
role in the identification, staging, and grading of malignant areas on biopsy
tissue specimens. High resolution histology images are subject to high variance
in appearance, sourcing either from the acquisition devices or the H\&E
staining process. Nuclei segmentation is an important task, as it detects the
nuclei cells over background tissue and gives rise to the topology, size, and
count of nuclei which are determinant factors for cancer detection. Yet, it is
a fairly time consuming task for pathologists, with reportedly high
subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern
Artificial Intelligence (AI) models enable the automation of nuclei
segmentation. This can reduce the subjectivity in analysis and reading time.
This paper provides an extensive review, beginning from earlier works use
traditional image processing techniques and reaching up to modern approaches
following the Deep Learning (DL) paradigm. Our review also focuses on the weak
supervision aspect of the problem, motivated by the fact that annotated data is
scarce. At the end, the advantages of different models and types of supervision
are thoroughly discussed. Furthermore, we try to extrapolate and envision how
future research lines will potentially be, so as to minimize the need for
labeled data while maintaining high performance. Future methods should
emphasize efficient and explainable models with a transparent underlying
process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table
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